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Practical Machine Learning for Computer Vision

244,000₫

Practical Machine Learning for Computer Vision

Sách gia công, Bìa mềm

This practical book shows you how to employ machine

learning models to extract information from images. ML engineers and

data scientists will learn how to solve a variety of image problems

including classification, object detection, autoencoders, image

generation, counting, and captioning with proven ML techniques. This

book provides a great introduction to end-to-end deep learning: dataset

creation, data preprocessing, model design, model training, evaluation,

deployment, and interpretability.

Google engineers Valliappa

Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop

accurate and explainable computer vision ML models and put them into

large-scale production using robust ML architecture in a flexible and

maintainable way. You'll learn how to design, train, evaluate, and

predict with models written in TensorFlow or Keras.

You'll learn how to:

• Design ML architecture for computer vision tasks

• Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task

• Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model

• Preprocess images for data augmentation and to support learnability

• Incorporate explainability and responsible AI best practices

• Deploy image models as web services or on edge devices

• Monitor and manage ML models

Thể loại:Computers - Artificial Intelligence (AI)

Năm:2021

In lần thứ:1

Nhà xuát bản:O'Reilly Media

Ngôn ngữ:english

Trang:481